1. Technical Field
The present invention relates to optic disk detection in retinal images, and more particularly, to a system and method for robust optic disk detection in retinal images using vessel structure and radon transform.
2. Discussion of the Related Art
Structural analysis of retinal images is critical to screening and diagnosis of eye diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Estimating the location and size of an optic disk is needed in clinical applications such as measuring the progression of glaucoma as described in L. Bonomi and N. Orzalesi, Glaucoma: Concepts in Evolution: Morphometric and Functional Parameters in the Diagnosis and Management of Glaucoma. Kugler Ghendini, 1991, and E. Corona, S. Mitra, M. Wilson, T. Krile, Y. Kwon and P. Soliz, “Digital stereo image analyzer for generating automated 3-d measures of optic disc deformation in glaucoma”, IEEE Transactions on Medical Imaging, vol. 21, no. 10, pp. 1244-1253, 2002; providing a reference point to a macular that is the critical region for diabetic retinopathy diagnosis as described in T. Dorion, Manual of Ocular Fundus Examination. Butterworth-Heinemann, 1998; and identifying a concentric area where an arteriolar-to-venular diameter ratio (AVR) is measured for cardiovascular disease diagnosis as described in L. Hubbard, R. Brothers and et al., “Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study”, Ophthalmology, vol. 106, no. 12, pp. 2269-2280, 1999, and H. Li, W. Hsu, M. Lee and T. Wong, “Automatic grading of retinal vessel caliber”, IEEE Transactions on Biomedical Engineering, vol. 52, no. 7, pp. 1352-1355, 2005. Detection of an optic disk usually includes two steps: the first is the localization of a candidate region or nerve head, which should be inside an optic disk and the second is the detection of a boundary of the optic disk.
In a retinal image, the optic disk appears to be a round-disk like region with high intensity values. Physiologically, an optic disk is where the vessels, including arteries and veins, emerge and branch out to form a tree structure. An illustration of an optic disk is given in FIG. 1. Optic disks appear to be the brightest part in most retinal images. This property has often been used to located possible candidate regions by clustering pixels with highest brightness values as described in H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images”, Proceedings of 9th IEEE International Conference on Computer Vision (ICCV), 2003, M. L. P. Pallawala, W. Hsu and K. G. Eone, “Automated optic disc localization and contour detection using ellipse fitting and wavelet transform”, Proceedings of 8th European Conference on Computer Vision (ECCV), 2004, E. Trucco and P. Kamat, “Locating the optic disk in retinal images via plausible detection and constraint satisfaction”, Proceedings of 2004 International Conference on Image Processing (ICIP04), 2004, and M. Lalonde, M. Beaulieu, and L. Gagnon, “Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching”, IEEE Transactions on Medical Imaging, vol. 20, no. 11, pp. 1193-1200, 200 1, or by using a matched filter or Hough transform to detect a round region as described in H. Li, W. Hsu, M. Lee and T. Wong, “Automatic grading of retinal vessel caliber”, IEEE Transactions on Biomedical Engineering, vol. 52, no. 7, pp. 1352-1355, 2005, R. Abdel-Ghafar, T. Morris, T. Ritchings and I. Wood, “Detection and characterisation of the optic disk in glaucoma and diabetic retinopathy”, Proceedings of Medical Image Understanding and Analysis, 2004, and A. Osareh, M. Mirmehdi, B. Thomas and R. Markham, “Colour morphology and snakes for optic disk localization”, Proceedings of Medical Image Understanding and Analysis, 2001. However, the assumption that an optic disk is the brightest area in a retinal image does not always hold. Counter examples are given in A. Hoover and M. H. Goldbuam, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 951-958, 2003, where lesions or hemorrhages in unhealthy eyes may obscure optic disks and generate bright round structures elsewhere. Even with healthy eyes, uneven illumination may cause some regions to be brighter, thus invalidating the assumption.
Compared with brightness-based methods, using vessel structure is more robust and stable for optic disk localization, since neither eye diseases nor uneven illumination changes the fact that the root of a vessel tree is always inside an optic disk. In A. Hoover and M. H. Goldbuam, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 951-958, 2003, the root of a vessel tree is assumed to have the highest vessel density and optic disks are detected by fuzzy convergence. A parabola is imposed on a retinal image as described in M. Foracchia, E. Grisan and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure”, IEEE Transactions on Medical Imaging, vol. 23, no. 10, pp. 1189-1195, 2004, to segment the vessels into four parts. The parameters of a parabola are obtained by maximizing the difference between the vessel directions inside and outside the parabola. The vertex of an optimized parabola is defined as the location of an optic disk. The vessel-based detection as described in C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms”, ACM Computing Survey, vol. 36, no. 2, pp. 81-121, 2004, is computationally intensive due to the requirement for accurate vessel detection as pointed out in H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images”, Proceedings of 9th IEEE International Conference on Computer Vision (ICCV), 2003. The assumption underlying the method described in A. Hoover and M. H. Goldbuam, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 951-958, 2003 may not always be true. For example, in retinal images with tortuosity, a non-optic disk area may have higher vessel density than inside the optic disk. As for the method in M. Foracchia, E. Grisan and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure”, IEEE Transactions on Medical Imaging, vol. 23, no. 10., pp. 1189-1195, 2004, parameters to be optimized in the objective function are chosen to be proportional to vessel caliber, and simulated annealing is adopted to optimize the objective function.